CN112180731B - Energy equipment operation control method and system - Google Patents
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Abstract
The invention discloses an energy equipment operation control method, which comprises the following steps that S1, a load tracking reference curve is established, a maximum value, a minimum value, a maximum value and a minimum value are extracted, the maximum value and the minimum value are differenced, the number of the maximum value and the number of the minimum value are summed, and the result of the differenced and the summed result are subjected to fuzzification processing to obtain fuzzification variable words; s2, calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and the fuzzy rule; s3, performing inverse modeling on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function; s4, changing control variables of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function, returning to the step S1 to update the load tracking reference curve, and circularly carrying out the steps S1 to S4; the time span of the load tracking reference curve is larger than the maximum value set by the prediction time domain, the prediction time domain can be adaptively updated, the calculated amount is reduced, and the control effect is improved.
Description
Technical Field
The disclosure relates to the field of energy system operation control, in particular to an energy equipment operation control method and system, which can be used for various energy systems including, but not limited to, boilers, generators, pyrolysis gasifiers, heat pumps and the like, and solves the problem that the energy systems are too high in economy or energy cost during load adjustment.
Background
The output of the energy system must be correspondingly adjusted along with the change of the load, and the load change of the current energy system is predictable in most cases, for example, the electricity load of an office building, the heating load in winter and the like generally change periodically. The existing energy system control thought is to use the difference between the system output and the load demand to carry out feedback adjustment on the system, and the larger the difference is, the stronger the feedback signal is, such as the most commonly used PID controller. The problem with this control concept is that when the load fluctuates strongly and frequently, the energy system must be adjusted frequently, and the cost of such frequent adjustment is high, such as the need to start and stop the equipment frequently, which wastes energy and damages equipment life. Therefore, it is necessary to develop an energy system operation control method, which not only ensures that the system output can meet the requirement of load change, but also ensures that the operation control cost is not too high.
Most of energy systems are complex systems with nonlinear, time-delay and multivariable coupling characteristics. Model Predictive Control (MPC) is an effective method of solving complex industrial process control and has been widely used in the field of energy system control. The prediction horizon is a key parameter of the MPC, and is influenced by the system inertia and the control target requirement, and too small prediction horizon can lead to the performance degradation of the controller, but too large prediction horizon can increase the calculation time without benefit to the control effect. In the prior art, a fixed prediction time domain method is often adopted, so that the calculated amount of a control system is large, the error is large, and the control effect is poor.
Disclosure of Invention
In the prior art, the problems of large calculation amount, large error and poor control effect exist in model prediction control, and in order to solve the technical problems, the present disclosure provides the following technical solutions.
An energy device operation control method comprises the following steps.
S1, a load tracking reference curve is established, the maximum value, the minimum value, the maximum value and the minimum value in the load tracking reference curve are extracted, the maximum value and the minimum value are subjected to difference, the number of the maximum values and the number of the minimum values are summed, and the result of the difference and the result of the summation are subjected to blurring processing to obtain a blurring variable word.
S2, calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and a preset fuzzy rule.
And S3, performing anti-fuzzy on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function.
S4, changing the control variable of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function, and
And returning to the step S1 to update the load tracking reference curve, and circularly carrying out the steps S1 to S4 until the tracking of the load tracking reference curve is finished.
Wherein the load tracking reference curve has a time span greater than the maximum value set in the prediction horizon.
According to some embodiments provided by the present disclosure, the start time of the load tracking reference curve is set to be t 0, the time span is Δt, the end time of the load tracking reference curve is set to be t 0 +Δt, the prediction time domain is set to be t f, the control time domain is set to be t c, in the step S4, the control variable of the energy device is changed within the time span of t 0+tc, and then the step S1 is returned to update the start time of the load tracking reference curve to be t 0+tc, and the end time of the load tracking reference curve is set to be t 0+tc +Δt.
According to some embodiments provided by the present disclosure, in the step S2, weights of a prediction time domain and one or more cost functions are calculated according to a preset fuzzy rule, and a weight duty ratio between the cost functions is adjusted according to the updated load tracking reference curve.
According to some embodiments provided in the present disclosure, in the step S2, the fuzzy variable word is subjected to fuzzy calculation by a fuzzy argument vocabulary corresponding to the fuzzy argument vocabulary, where the fuzzy argument vocabulary includes a difference between a maximum value and a minimum value and a sum of maximum value and minimum value points, and the fuzzy argument vocabulary includes "negative big", "negative middle", "negative small", "zero", "positive small", "median" and "positive big".
According to some embodiments provided in the present disclosure, in the step S3, a maximum membership value method, a weighted average gravity center method, or a median method is used to perform a weight defuzzification process on the prediction time domain and the cost function to obtain an exact value of the control time domain and an exact value of the weight of the cost function.
According to some embodiments provided in the present disclosure, the weighted average gravity center method is used for performing an anti-blurring process, and the calculation formula includes:
Where x 0 is the exact value obtained by the anti-fuzzy treatment, x i is the value in the anti-fuzzy theory domain, and μ (x i) is the membership value of x i.
The disclosure also provides an energy device operation control system, which comprises a blurring processing unit, a blurring calculation unit, an anti-blurring processing unit, a blurring control unit and a blurring control feedback unit.
The blurring processing unit is used for establishing a load tracking reference curve, extracting the maximum value, the minimum value difference, the maximum value and the minimum value in the load tracking reference curve, making the maximum value difference with the minimum value difference, summing the number of the maximum values and the number of the minimum values, and blurring the result to obtain a blurring variable word.
The fuzzy calculation unit is used for receiving the fuzzified variable words and calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and a preset fuzzy rule.
The anti-fuzzy processing unit is used for receiving the membership degree and performing anti-fuzzy on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function.
The fuzzy control unit is used for receiving the exact value of the control time domain and the exact value of the weight of the cost function and controlling the control time domain and the control variable of the energy source device.
The fuzzy control feedback unit is used for collecting actual load data of the energy system and the control time domain information so as to update the load tracking reference curve, and the time span of the load tracking reference curve is larger than the maximum value set by the prediction time domain.
According to some embodiments provided by the present disclosure, the blurring calculation unit obtains a plurality of cost functions after blurring calculation, and a sum of weights of the plurality of cost functions is equal to one.
According to some embodiments provided by the present disclosure, the blurring processing unit includes a blurring argument field of a difference between a maximum value and a minimum value and a sum of a maximum value point and a minimum value point, and a blurring argument vocabulary corresponding to the blurring argument field.
According to some embodiments provided by the present disclosure, the fuzzy rule establishes a relationship between a state variable and a control variable in the form of a fuzzy conditional sentence, so as to calculate an input fuzzy amount to obtain a corresponding output fuzzy amount.
According to the technical scheme, the energy equipment operation control method provided by the disclosure can adaptively update and predict the weight of the time domain and the cost function through fuzzy control according to the load tracking reference curve characteristics, so that the calculated amount of the control system is greatly reduced, and the control effect of the energy equipment can be improved.
Drawings
FIG. 1 schematically illustrates a flow chart of an energy device operation control method of an embodiment of the present disclosure;
FIG. 2 schematically illustrates a comparison graph of gas production load output from a 75kW biomass gasifier with/without fuzzy control in an energy plant operation control method of an embodiment of the present disclosure;
FIG. 3 schematically illustrates a control parameter comparison graph of a 75kW biomass gasifier with/without fuzzy control in an energy device operation control method of an embodiment of the disclosure;
FIG. 4 schematically illustrates a control variable variation comparison graph of a 75kW biomass gasifier with/without fuzzy control in an energy device operation control method of an embodiment of the disclosure;
Fig. 5 schematically illustrates a partial performance index comparison graph of a 75kW biomass gasifier with/without fuzzy control in an energy device operation control method of an embodiment of the disclosure.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It may be evident, however, that one or more embodiments may be practiced without these specific details. In the following description, descriptions of well-known techniques are omitted so as not to unnecessarily obscure the concept of the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "comprising" as used herein indicates the presence of a feature, step, operation, but does not preclude the presence or addition of one or more other features.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed as having meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner, such as the field of discussion, which refers to the collection of all selectable values comprising a control variable or control target, generally represented by the letter U; for example, exact amounts, true input and output values; for example, the fuzzy quantity (or fuzzy variable words) refers to input values and output values expressed by using fuzzy linguistic variables, wherein the input values and the output values generally comprise 'negative big' (NB), 'negative middle' (NM), 'negative small' (NS), 'zero' (ZO), 'positive small' (PS), 'median' (PM), 'positive big' (PB) and the like, the quantity of the linguistic variables is generally determined according to the precision requirement of fuzzy control, and the more the linguistic variables are, the higher the precision of control is; for example, the membership degree refers to that if a number mu (x) epsilon [0,1] corresponds to any element x in the universe, mu is called a fuzzy set on U, mu (x) is called the membership degree of x to mu, classical set pair things are simply classified by using 1 and 0 to indicate that the items belong to or do not belong to, membership degree in the fuzzy set is a value continuously changed between 0 and 1, the closer the membership degree mu (x) is to 1, the higher the degree that the x belongs to mu is, and the closer the mu (x) is to 0, the lower the degree that the x belongs to mu is; for example, when x varies in U, μ (x) is a function called μmembership function, and the membership degree of μ is represented by the membership function μ (x) with a value in the interval [1,0], where the membership function commonly used in the fuzzy control process can be classified into membership functions such as rectangular, trapezoidal triangle, curve distribution, etc. according to the shape.
Fig. 1 schematically illustrates a flowchart of an energy device operation control method according to an embodiment of the present disclosure.
As shown in fig. 1, the present disclosure provides an energy device operation control method, including the steps of:
S1, a load tracking reference curve is established, the maximum value, the minimum value, the maximum value and the minimum value in the load tracking reference curve are extracted, the maximum value and the minimum value are differenced, the number of the maximum values and the number of the minimum values are summed, and the result of the differenced and the summed result are subjected to fuzzification processing to obtain fuzzified variable words;
S2, calculating the membership degree of the weight of the prediction time domain and the cost function according to the fuzzified variable words and a preset fuzzy rule;
s3, performing inverse modeling on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function;
S4, changing the control variable of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function, and
Returning to the step S1 to update the load tracking reference curve, and circularly carrying out the steps S1 to S4 until the tracking of the load tracking reference curve is finished;
wherein the time span of the load tracking reference curve is greater than the maximum value set in the prediction time domain.
According to some embodiments provided by the present disclosure, corresponding load values are set at a plurality of fixed time points at intervals, and the load values at adjacent time points are sequentially connected, i.e. the tracking reference curve of the corresponding load.
According to some embodiments provided by the present disclosure, data of a maximum value and a minimum value in a tracking reference curve of a load are extracted and differenced, and the magnitude of the difference value indicates that the energy source equipment controls the magnitude of the adjustment amplitude in the period of time; and extracting maximum and minimum values in the tracking reference curve of the load, summing the numbers of the maximum and minimum values, and indicating the frequency of energy source equipment control adjustment in the period of time.
According to some embodiments provided by the present disclosure, the start time of the load tracking reference curve is set to be t 0, the time span is Δt, the end time of the load tracking reference curve is t 0 +Δt, the prediction time domain is t f, the control time domain is t c, in step S4, the control variable of the energy device is changed within the time span of t 0+tc, then step S1 is returned, the start time of the load tracking reference curve is updated to be t 0+tc, and the end time is t 0+tc +Δt.
According to some embodiments provided by the present disclosure, in step S2, weights of a prediction time domain and one or more cost functions are calculated through a preset fuzzy rule, and a weight duty ratio between the cost functions is adjusted according to an updated load tracking reference curve.
According to some embodiments provided by the present disclosure, the cost function includes an error in the actual load from the load on the tracking reference curve, an operating cost, or an energy efficiency of the device.
According to some embodiments provided in the present disclosure, in step S2, fuzzy variable words are subjected to fuzzy computation by a fuzzy argument vocabulary corresponding to the fuzzy argument vocabulary including differences between maximum and minimum values and sums of maximum and minimum value points, and the fuzzy argument vocabulary including "negative big", "negative middle", "negative small", "zero", "positive small", "median" and "positive big".
According to some embodiments provided in the present disclosure, in step S3, the maximum membership value method, the weighted average gravity method, or the median method is used to deblur the weights of the prediction time domain and the cost function to obtain the exact value of the control time domain and the exact value of the weight of the cost function.
According to some embodiments provided by the present disclosure, the anti-blurring process is performed by using a weighted average gravity center method, and the calculation formula includes:
Where x 0 is the exact value obtained by the anti-fuzzy treatment, x i is the value in the anti-fuzzy theory domain, and μ (x i) is the membership value of x i.
And then changing the control variable of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function. Optionally, a multi-objective optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, etc.) is used to solve a plurality of effective solutions of a multi-objective optimization problem formed by a weighted cost function, where each effective solution includes a corresponding feed amount and an air intake amount in a control time domain. And selecting an optimal solution from a plurality of effective solutions by utilizing a multi-objective decision method (such as a linear weighting sum method, a fuzzy optimization method and the like), changing control variables of energy equipment according to the optimal solution, updating the established load tracking reference curve, and circularly carrying out the steps until the tracking of the load tracking reference curve is finished.
The disclosure also provides an energy device operation control system, which comprises a blurring processing unit, a blurring calculation unit, an anti-blurring processing unit, a blurring control unit and a blurring control feedback unit.
And the blurring processing unit is used for establishing a load tracking reference curve, extracting the difference between the maximum value and the minimum value in the load tracking reference curve, the maximum value and the minimum value, making the difference between the maximum value and the minimum value, summing the number of the maximum value and the number of the minimum value, and blurring the result to obtain a blurring variable word.
And the fuzzy calculation unit is used for receiving the fuzzified variable words and calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and a preset fuzzy rule.
And the anti-blurring processing unit is used for receiving the membership degree and performing anti-blurring on the membership degree to obtain an exact value of the control time domain and an exact value of the weight of the cost function.
And the fuzzy control unit is used for receiving the exact value of the control time domain and the exact value of the weight of the cost function and controlling the control time domain and the control variable of the energy source device to be changed.
And the fuzzy control feedback unit is used for collecting actual load data of the energy system and controlling time domain information so as to update a load tracking reference curve, and the time span of the load tracking reference curve is larger than the maximum value set by the prediction time domain.
Specifically, the energy device operation control system performs the adjustment control operation by the following steps. According to the embodiments of the present disclosure, it should be noted that the energy device operation control system and the energy device operation control method provided by the present disclosure correspond to each other, and the energy device operation control system may be used to implement the energy device operation control method.
S1, a load tracking reference curve is established through a blurring processing unit, the maximum value, the minimum value, the maximum value and the minimum value in the load tracking reference curve are extracted, the maximum value and the minimum value are differenced, the number of the maximum values and the number of the minimum values are summed, and the result of the differenced and the summed result are subjected to blurring processing to obtain a blurring variable word.
S2, calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and a preset fuzzy rule by a fuzzy calculation unit;
s3, performing inverse modeling on the membership degree by an inverse modeling processing unit to obtain an exact value of a control time domain and an exact value of a weight of a cost function;
S4, the fuzzy control unit changes the control variable of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function, and
Returning to the step S1, updating the load tracking reference curve by the fuzzy control feedback unit, and circularly carrying out the steps S1 to S4 until the tracking of the load tracking reference curve is finished;
wherein the time span of the load tracking reference curve is greater than the maximum value set in the prediction time domain.
According to some embodiments provided by the present disclosure, the blurring calculation unit obtains a plurality of cost functions after blurring calculation, and a sum of weights of the plurality of cost functions is equal to one.
According to some embodiments provided by the present disclosure, the blurring processing unit includes a blurring field of a difference between a maximum value and a minimum value and a sum of a maximum value point and a minimum value point, and a blurring variable word set corresponding to the blurring field.
According to some embodiments provided by the present disclosure, the fuzzy rules establish a relationship between the state variable and the control variable in the form of a fuzzy conditional sentence, so as to calculate the input fuzzy amount to obtain a corresponding output fuzzy amount.
According to the technical scheme, the energy equipment operation control method provided by the disclosure can adaptively update the prediction time domain according to the load tracking reference curve characteristics, so that the calculated amount of a control system is greatly reduced, and meanwhile, the control effect is improved to a certain extent.
The technical solutions of the present disclosure are described below in conjunction with some specific embodiments, and it should be understood that these specific embodiments are merely for better and clearer explanation of the technical solutions of the present disclosure, so as to facilitate understanding of the technical solutions of the present disclosure by those skilled in the art, and should not be considered as limiting the scope of protection of the present disclosure.
According to some embodiments provided by the present disclosure, a 75kW biomass gasifier is selected as the energy source device, wherein the raw material is wood chips.
The gas production load tracking reference curve for a 75kW biomass gasifier was plotted according to the parameters in table 1. Specifically, the gas production load corresponding to each time point in table 1 is connected through a broken line, and the gas production load tracking reference curve is obtained.
Table 1 references the change in gas production load over time
The control variables of the 75kW biomass gasification furnace comprise air inflow and feeding amount, wherein the control range of the air inflow is 9.5-16.5 m 3/h, and the control range of the feeding amount is 0-100 kg/h.
The first cost function of a 75kW biomass gasifier is the difference between the actual output gas load of the biomass gasifier and the gas load on the reference curve. The smaller this difference, the better the control, the higher the accuracy.
The second cost function of a 75kW biomass gasifier is: the smaller the sum of the actual air inflow of the biomass gasification furnace and the adjacent moment change amplitude of the feeding amount, the more accurate the control effect is, the lower the abrasion of the air inlet device and the feeding device is, and meanwhile, the lower the energy consumption is.
The third cost function of a 75kW biomass gasifier is: the sum of the actual gas production heat value, the gasification temperature and the gasification efficiency of the biomass gasification furnace is larger, which indicates that the better the control effect is, namely the efficiency of the biomass gasification furnace can be improved.
All variables involved in the three cost functions are normalized before operation, and the three cost functions are multiplied by the weights of the three cost functions respectively and then optimized in the MPC.
Under the condition of no fuzzy control, the control time domain of the 75kW biomass gasification furnace is equal to the prediction time domain, and is 20min, and the weight of the first cost function is as follows: weights of the second cost function: weight of the third cost function = 1:0:0.
With fuzzy control, the control time domain of the 75kW biomass gasification furnace is equal to the prediction time domain, the weight of the second cost function=the weight of the third cost function= (1-the weight of the first cost function)/2.
Setting the time span delta t to be 30min, collecting data about maximum value, minimum value, maximum value and minimum value in a gas production load tracking reference curve of the biomass gasification furnace from operation to 30min, making difference between the maximum value and the minimum value, summing the number of the maximum value and the number of the minimum value, and carrying out fuzzification treatment on the result of making difference and the result of summing to obtain a fuzzification variable word. As shown in table 2.
TABLE 2 fuzzification discourse and fuzzification variable vocabulary
The fuzzy variable vocabulary includes "negative large" (NB), "negative medium" (NM), "negative small" (NS), "zero" (ZO), "positive small" (PS), "median" (PM), "positive large" (PB), etc. The number of the language variables is generally determined according to the precision requirement of fuzzy control, and the more the language variables are, the higher the precision of control is.
And calculating the membership degrees of the prediction time domain, the weight of the first cost function, the weight of the second cost function and the weight of the third cost function according to the fuzzified variable words and a preset fuzzy rule.
Wherein, the preset fuzzy rule is shown in table 3.
TABLE 3 fuzzy rule TABLE
And performing inverse modeling on the obtained membership degree to obtain an exact value of the control time domain and an exact value of the first cost function weight.
The exact values of the defuzzified fuzzy variable vocabulary and the control time domain of the output and the exact values of the first cost function weights are shown in table 4.
TABLE 4 control time domain and first cost function weight argument domain of defuzzified variable vocabulary and output
In the process of performing anti-blurring calculation, a weighted average gravity center method is adopted, and the formula is as follows:
Wherein: x 0 is the exact value after defuzzification of the fuzzy control output, x i is the value in the fuzzy control quantity theory domain, and μ (x i) is the membership value of x i.
And according to the calculated exact value of the control time domain and the exact value of the weight of the cost function, changing the control variable of the 75kW biomass gasifier according to the exact value of the control time domain and the exact value of the weight of the cost function. Optionally, a multi-objective optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, etc.) is used to solve a plurality of effective solutions of a multi-objective optimization problem formed by three weighted cost functions, where each effective solution includes a corresponding feed amount and an air intake amount in a control time domain. And selecting an optimal solution from a plurality of effective solutions by utilizing a multi-objective decision method (such as a linear weighting sum method, a fuzzy optimization method and the like), changing control variables of energy equipment according to the optimal solution, updating the established gas production load tracking reference curve, and circularly carrying out the steps until the gas production load tracking reference curve is tracked. In this embodiment, the duration of the entire gas production load trace is set to 50 minutes.
Fig. 2 schematically illustrates a comparison graph of the gas production load output by a biomass gasifier with/without a fuzzy control module in an energy device operation control method according to an embodiment of the disclosure.
Specifically, as shown in fig. 2, fig. 2 (a) is a comparison graph of a gas production load curve of a 75kW biomass gasification furnace with/without fuzzy control and an established load tracking reference curve, and fig. 2 (b) is an error comparison graph of the gas production load curve of the 75kW biomass gasification furnace with/without fuzzy control and the established load tracking reference curve, it can be seen from the graph that the curve with fuzzy control is more fit to the established load tracking reference curve than the curve without fuzzy control, especially over time, the error is continuously lower, and the fluctuation variation of the error is smaller, that is, in the technical scheme of the disclosure, the fuzzy control feedback unit is improved and improved after updating the load tracking reference curve according to the acquired actual load data of the energy system and the control time domain information.
Fig. 3 schematically illustrates a control parameter comparison chart of a biomass gasifier with/without fuzzy control in an energy device operation control method according to an embodiment of the disclosure.
Specifically, fig. 3 (a) is a comparison chart of a predicted time domain of a 75kW biomass gasifier with/without fuzzy control, fig. 3 (b) is a comparison chart of a first cost function weight of the 75kW biomass gasifier with/without fuzzy control, and it can be concluded from fig. 3 that the predicted time domain of the 75kW biomass gasifier with fuzzy control is dynamically changed (8-12 min) and is smaller than the MPC predicted time domain without fuzzy control module (20 min), so that the technical scheme provided by the disclosure can effectively reduce the calculation amount of the MPC control system.
Fig. 4 schematically illustrates a control variable variation comparison chart of a biomass gasifier with/without fuzzy control in an energy device operation control method according to an embodiment of the present disclosure.
Specifically, fig. 4 (a) is a graph comparing variances of intake air amounts of a 75kW biomass gasification furnace with/without fuzzy control, and fig. 4 (b) is a graph comparing variances of intake air amounts of a 75kW biomass gasification furnace with/without fuzzy control, wherein variances of intake air amounts of the 75kW biomass gasification furnace with fuzzy control are 3.88 and are smaller than variances of intake air amounts of 6.08 with no fuzzy control; the variance of the feed rate of the 75kW biomass gasifier with fuzzy control is 155.53, which is less than the variance 165.18 of the feed rate without fuzzy control. Therefore, the technical scheme provided by the disclosure can effectively reduce the variation amplitude of the air inflow and the feeding amount, reduce the energy consumption of the equipment and the abrasion of the equipment, and also can reduce the running cost of the equipment.
Fig. 5 schematically illustrates a partial performance index comparison chart of a biomass gasifier with/without fuzzy control in an energy device operation control method according to an embodiment of the disclosure.
Specifically, fig. 5 (a) is a graph comparing the calorific value of gas produced by a 75kW biomass gasification furnace with/without fuzzy control, fig. 5 (b) is a graph comparing gasification temperature of the 75kW biomass gasification furnace with/without fuzzy control, and fig. 5 (c) is a graph comparing gasification efficiency of the 75kW biomass gasification furnace with/without fuzzy control. As can be seen from the figure, the gasification efficiency of the 75kW biomass gasification furnace with/without fuzzy control is substantially equivalent, but the gas production heating value and the gasification temperature with fuzzy control are both higher than those without fuzzy control. Therefore, it can be concluded that the technical scheme provided by the disclosure can effectively improve the heating value and the gasification temperature of the produced gas under the condition of keeping the gasification efficiency unchanged, namely, the performance of the energy equipment is improved.
In the above embodiment, the duration of the entire gas production load trace is 50min, and the time span Δt is set to 30min. In other embodiments of the present disclosure, other overall gas production load tracking durations or time spans may be provided, with results consistent with the embodiments described above.
In other embodiments of the disclosure, different load tracking reference curves may be established for different loads, different control variables may be controlled, and corresponding weights and fuzzy variable words may be given to one or more cost functions according to actual needs, and multiple variable arrangements may be combined into different embodiments, which are all within the scope of the disclosure.
Thus, embodiments of the present disclosure have been described in detail with reference to the accompanying drawings. It should be noted that, in the drawings or the text of the specification, implementations not shown or described are all forms known to those of ordinary skill in the art, and not described in detail. Furthermore, the above definitions of the steps/modules are not limited to the specific steps and module names mentioned in the embodiments, and may be simply modified or replaced by those of ordinary skill in the art.
It should also be noted that, in the specific embodiments of the disclosure, unless otherwise noted, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present disclosure. In particular, all numbers expressing dimensions, range conditions, and so forth, used in the specification and claims are to be understood as being modified in all instances by the term "about". In general, the meaning of expression is meant to include a variation of + -10% in some embodiments, a variation of + -5% in some embodiments, a variation of + -1% in some embodiments, and a variation of + -0.5% in some embodiments by a particular amount.
Those skilled in the art will appreciate that the features recited in the various embodiments of the invention and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the invention. In particular, the features recited in the various embodiments of the invention and/or in the claims can be combined in various combinations and/or combinations without departing from the spirit and teachings of the invention. All such combinations and/or combinations fall within the scope of the invention.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the invention thereto, but to limit the invention thereto, and any modifications, equivalents, improvements and equivalents thereof may be made without departing from the spirit and principles of the invention.
Claims (6)
1. An energy device operation control method is characterized by comprising the following steps:
s1, a load tracking reference curve is established, a maximum value, a minimum value, a maximum value and a minimum value in the load tracking reference curve are extracted, the maximum value and the minimum value are subjected to difference, the number of the maximum values and the number of the minimum values are summed, and the result of the difference and the result of the summation are subjected to blurring processing to obtain a blurring variable word;
S2, calculating the membership degree of the weight of the prediction time domain and the cost function according to the fuzzified variable words and a preset fuzzy rule, wherein the method comprises the following steps:
calculating the weight of a prediction time domain and one or more cost functions through a preset fuzzy rule, and adjusting the weight duty ratio between the cost functions according to the updated load tracking reference curve; and
Performing fuzzy calculation on the fuzzy variable words through a fuzzy universe and a fuzzy variable word set corresponding to the fuzzy universe, wherein the fuzzy universe comprises the difference between a maximum value and a minimum value and the sum of the number of points of the maximum value and the minimum value, and the fuzzy variable word set comprises 'negative big', 'negative medium', 'negative small', 'zero', 'positive small', 'median' and 'positive big';
s3, performing inverse fuzzy on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function, wherein the method comprises the following steps: performing weight defuzzification on the prediction time domain and the cost function by adopting a maximum membership value method, a weighted average gravity center method or a median method to obtain an exact value of a control time domain and an exact value of a weight of the cost function;
S4, changing the control variable of the energy equipment according to the exact value of the control time domain and the exact value of the weight of the cost function, and
Returning to the step S1 to update the load tracking reference curve, and circularly carrying out the steps S1 to S4 until the tracking of the load tracking reference curve is finished;
Wherein the time span of the load tracking reference curve is greater than the maximum value set by the prediction time domain;
The establishing the load tracking reference curve comprises the following steps: setting the starting time of the load tracking reference curve as t 0, the time span as deltat, the ending time of the load tracking reference curve as t 0 + deltat, the prediction time domain as t f and the control time domain as t c, in the step S4, changing the control variable of the energy source equipment within the time span of t 0+tc, and returning to the step S1, wherein the starting time of the load tracking reference curve is t 0+tc, and the ending time is t 0+tc + deltat.
2. The energy device operation control method according to claim 1, wherein the anti-blurring process is performed by using the weighted average gravity center method, and the calculation formula includes:
Where x 0 is the exact value obtained by the anti-fuzzy treatment, x i is the value in the anti-fuzzy theory domain, and μ (x i) is the membership value of x i.
3. An energy device operation control system for executing the energy device operation control method according to claim 1 or 2, characterized by comprising:
The blurring processing unit is used for establishing a load tracking reference curve, extracting the maximum value, the minimum value difference, the maximum value and the minimum value in the load tracking reference curve, making the maximum value difference with the minimum value difference, summing the number of the maximum values and the number of the minimum values, and blurring the result to a blurring variable word;
the fuzzy calculation unit is used for receiving the fuzzified variable words and calculating the membership degree of the predicted time domain and the weight of the cost function according to the fuzzified variable words and a preset fuzzy rule;
The anti-blurring processing unit is used for receiving the membership degree and performing anti-blurring on the membership degree to obtain an exact value of a control time domain and an exact value of a weight of a cost function;
A fuzzy control unit for receiving the exact value of the control time domain and the exact value of the weight of the cost function, and controlling the control time domain and the control variable of the energy device;
and the fuzzy control feedback unit is used for collecting the actual load data of the energy equipment and the control time domain information so as to update the load tracking reference curve, and the time span of the load tracking reference curve is larger than the maximum value set by the prediction time domain.
4. A system according to claim 3, wherein the blurring calculation unit obtains a plurality of cost functions after blurring calculation, and a sum of weights of the plurality of cost functions is equal to one.
5. The system according to claim 3 or 4, wherein the blurring processing unit includes a blurring argument field of a difference between a maximum value and a minimum value and a sum of a maximum value point and a minimum value point, and a blurring argument vocabulary corresponding to the blurring argument field.
6. The system according to claim 3 or 4, wherein the fuzzy rule establishes a relationship between the state variable and the control variable in the form of a fuzzy conditional sentence for calculating the input fuzzy amount to obtain the corresponding output fuzzy amount.
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